Some content classification techniques require an initial collection of a large amount of training data from a threshold number of users. Content (e.g., a product recommendation) is then classified for one user based on how other users have reacted to the content. For example, an advertising message may be deemed a spam message for one user when 50 other users have identified the advertising message as a spam message.
Difficulties for using these generic classification techniques abound, however. First, accumulating a large amount of training data may require a lengthy ramp-up time, rendering a classification system less capable of producing meaningful results until much later and thus risking user retention.
Second, users who shared similar interests in the past may develop different interest profiles of their own over time, i.e., growing apart from each other. Using a same classification model for a large number of users may therefore run the risk of ignoring some users' specific interests, diminishing user experience for those users.
There is therefore a need for more individualized electronic messages classification techniques.